Solar Physics

, 293:48 | Cite as

Flare Prediction Using Photospheric and Coronal Image Data

  • Eric JonasEmail author
  • Monica Bobra
  • Vaishaal Shankar
  • J. Todd Hoeksema
  • Benjamin Recht


The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar-image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that i) automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, ii) combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and iii) classifies these features to predict whether a solar active region will flare within a time period of \(T\) hours, where \(T = 2 \mbox{ and }24\). Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We find that when optimizing for the True Skill Score (TSS), photospheric vector-magnetic-field data combined with flaring history yields the best performance, and when optimizing for the area under the precision–recall curve, all of the data are helpful. Our model performance yields a TSS of \(0.84 \pm0.03\) and \(0.81 \pm0.03\) in the \(T = 2\)- and 24-hour cases, respectively, and a value of \(0.13 \pm0.07\) and \(0.43 \pm0.08\) for the area under the precision–recall curve in the \(T=2\)- and 24-hour cases, respectively. These relatively high scores are competitive with previous attempts at solar prediction, but our different methodology and extreme care in task design and experimental setup provide an independent confirmation of these results. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. We believe that this is the first attempt to predict solar flares using photospheric vector-magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona, and it points the way towards greater data integration across diverse sources in future work.



The data used here are courtesy of the GOES team and the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) science teams of the NASA Solar Dynamics Observatory. This work was supported by NASA Grant NAS5-02139 (HMI), and in part by DHS Award HSHQDC-16-3-00083, NSF CISE Expeditions Award CCF-1139158, DOE Award SN10040 DE-SC0012463, and DARPA XData Award FA8750-12-2-0331, and gifts from Amazon Web Services, Google, IBM, SAP, The Thomas and Stacey Siebel Foundation, Apple Inc., Arimo, Blue Goji, Bosch, Cisco, Cray, Cloudera, Ericsson, Facebook, Fujitsu, HP, Huawei, Intel, Microsoft, Mitre, Pivotal, Samsung, Schlumberger, Splunk, State Farm and VMware. B. Recht is supported by NSF award CCF-1359814, ONR awards N00014-14-1-0024 and N00014-17-1-2191, the DARPA Fundamental Limits of Learning (Fun LoL) Program, a Sloan Research Fellowship, and a Google Faculty Award.

Disclosure of Potential Conflicts of Interest

The authors declare they have no conflicts of interest.


  1. Ahmed, O.W., Qahwaji, R., Colak, T., Higgins, P.a., Gallagher, P.T., Bloomfield, D.S.: 2013, Solar flare prediction using advanced feature extraction, machine learning, and feature selection. Solar Phys. 283, 157. DOI. ADSCrossRefGoogle Scholar
  2. Barnes, G., Leka, K.D.: 2008, Evaluating the performance of solar flare forecasting methods. Astrophys. J. Lett. 688, L107. DOI. ADS. ADSCrossRefGoogle Scholar
  3. Benz, A.O.: 2017, Flare observations. Living Rev. Solar Phys. 14(1), 2. DOI. ADSCrossRefGoogle Scholar
  4. Bishop, C.M.: 2006, Pattern Recognition and Machine Learning, Springer, New York. zbMATHGoogle Scholar
  5. Bloomfield, D.S., Higgins, P.a., McAteer, R.T.J., Gallagher, P.T.: 2012, Toward reliable benchmarking of solar flare forecasting methods. Astrophys. J. 747, L41. DOI. ADSCrossRefGoogle Scholar
  6. Bobra, M.G., Couvidat, S.: 2015, Solar flare prediction using SDO/HMI vector magnetic field data and a machine learning algorithm. Astrophys. J. 798, 135. DOI. ADSCrossRefGoogle Scholar
  7. Bobra, M.G., Sun, X., Hoeksema, J.T., Turmon, M., Liu, Y., Hayashi, K., Barnes, G., Leka, K.D.: 2014, The Helioseismic and Magnetic Imager (HMI) vector magnetic field pipeline: SHARPs – space-weather HMI active region patches. Solar Phys. 289(9), 3549. DOI. ADSCrossRefGoogle Scholar
  8. Boucheron, L.E., Al-Ghraibah, A., McAteer, R.T.J.: 2015, Prediction of solar flare size and time-to-flare using support vector machine regression. Astrophys. J. 812, 51. DOI. ADS. ADSCrossRefGoogle Scholar
  9. Canfield, R.C., Hudson, H.S., McKenzie, D.E.: 1999, Sigmoidal morphology and eruptive solar activity. Geophys. Res. Lett. 26(6), 627. DOI. ADSCrossRefGoogle Scholar
  10. Cho, Y., Saul, L.K.: 2009, Kernel methods for deep learning. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Adv Neural Info Proc Sys, 342. Google Scholar
  11. Crown, M.D.: 2012, Validation of the NOAA Space Weather Prediction Center’s solar flare forecasting look-up table and forecaster-issued probabilities. Space Weather 10, S06006. DOI. ADS. ADSCrossRefGoogle Scholar
  12. Falconer, D.A., Moore, R.L., Barghouty, A.F., Khazanov, I.: 2012, Prior flaring as a complement to free magnetic energy for forecasting solar eruptions. Astrophys. J. 757, 32. DOI. ADS. ADSCrossRefGoogle Scholar
  13. Fisher, G.H., Bercik, D.J., Welsch, B.T., Hudson, H.S.: 2012, Global forces in eruptive solar flares: the Lorentz force acting on the solar atmosphere and the solar interior. Solar Phys. 277, 59. DOI. ADS. ADSCrossRefGoogle Scholar
  14. Fletcher, L., Dennis, B.R., Hudson, H.S., Krucker, S., Phillips, K., Veronig, A., Battaglia, M., Bone, L., Caspi, A., Chen, Q., Gallagher, P., Grigis, P.T., Ji, H., Liu, W., Milligan, R.O., Temmer, M.: 2011, An observational overview of solar flares. Space Sci. Rev. 159, 19. DOI. ADS. ADSCrossRefGoogle Scholar
  15. Gabor, D.: 1947, Theory of communication. J. Inst. Electr. Eng., Part I, Gen. 94(73), 58. DOI. Google Scholar
  16. Garcia, H.A.: 1994, Temperature and emission measure from GOES soft X-ray measurements. Solar Phys. 154, 275. DOI. ADS. ADSCrossRefGoogle Scholar
  17. Georgoulis, M.K., Rust, D.M.: 2007, Quantitative forecasting of major solar flares. Astrophys. J. Lett. 661, L109. DOI. ADS. ADSCrossRefGoogle Scholar
  18. Hanser, F.A., Sellers, F.B.: 1996, Design and calibration of the goes-8 solar X-ray sensor: the XRS. Proc. SPIE 2812, 344. DOI. ADSCrossRefGoogle Scholar
  19. Hoeksema, J.T., Liu, Y., Hayashi, K., Sun, X., Schou, J., Couvidat, S., Norton, A., Bobra, M., Centeno, R., Leka, K.D., Barnes, G., Turmon, M.: 2014, The Helioseismic and Magnetic Imager (HMI) vector magnetic field pipeline: overview and performance. Solar Phys. 289, 3483. DOI. ADSCrossRefGoogle Scholar
  20. Jing, J., Song, H., Abramenko, V., Tan, C., Wang, H.: 2006, The statistical relationship between the photospheric magnetic parameters and the flare productivity of active regions. Astrophys. J. 644, 1273. DOI. ADS. ADSCrossRefGoogle Scholar
  21. Jonas, E., Pu, Q., Venkataraman, S., Stoica, I., Recht, B.: 2017, Occupy the cloud: distributed computing for the 99%. In: Proc. 2017 Symp. Cloud Computing, 445. ACM, New York. Google Scholar
  22. Kamarainen, J.K., Kyrki, V., Kälviäinen, H.: 2006, Invariance properties of Gabor filter-based features – overview and applications. IEEE Trans. Image Process. 15(5), 1088. DOI. ADSCrossRefGoogle Scholar
  23. Leka, K.D., Barnes, G.: 2003, Photospheric magnetic field properties of flaring versus flare-quiet active regions. II. Discriminant analysis. Astrophys. J. 595(2), 1296. DOI. ADSCrossRefGoogle Scholar
  24. Leka, K.D., Barnes, G.: 2007, Photospheric magnetic field properties of flaring versus flare-quiet active regions. IV. A statistically significant sample. Astrophys. J. 656, 1173. DOI. ADS. ADSCrossRefGoogle Scholar
  25. Lemen, J.R., Title, A.M., Akin, D.J., Boerner, P.F., Chou, C., Drake, J.F., Duncan, D.W., Edwards, C.G., Friedlaender, F.M., Heyman, G.F., Hurlburt, N.E., Katz, N.L., Kushner, G.D., Levay, M., Lindgren, R.W., Mathur, D.P., McFeaters, E.L., Mitchell, S., Rehse, R.A., Schrijver, C.J., Springer, L.A., Stern, R.A., Tarbell, T.D., Wuelser, J.-P., Wolfson, C.J., Yanari, C., Bookbinder, J.A., Cheimets, P.N., Caldwell, D., Deluca, E.E., Gates, R., Golub, L., Park, S., Podgorski, W.A., Bush, R.I., Scherrer, P.H., Gummin, M.A., Smith, P., Auker, G., Jerram, P., Pool, P., Soufli, R., Windt, D.L., Beardsley, S., Clapp, M., Lang, J., Waltham, N.: 2012, The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Solar Phys. 275, 17. DOI. ADS. ADSCrossRefGoogle Scholar
  26. Mairal, J., Koniusz, P., Harchaoui, Z., Schmid, C.: 2014, Convolutional kernel networks. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Adv. Neural Info. Proc. Sys., 27, 2627. Google Scholar
  27. Mason, J.P., Hoeksema, J.T.: 2010, Testing automated solar flare forecasting with 13 years of Michelson Doppler Imager magnetograms. Astrophys. J. 723(1), 634. DOI. ADSCrossRefGoogle Scholar
  28. Metcalf, T.R.: 1994, Resolving the 180-degree ambiguity in vector magnetic field measurements: the ‘minimum’ energy solution. Solar Phys. 155, 235. DOI. ADS. ADSCrossRefGoogle Scholar
  29. SunPy Community, Mumford, S.J., Christe, S., Pérez-Suárez, D., Ireland, J., Shih, A.Y., Inglis, A.R., Liedtke, S., Hewett, R.J., Mayer, F., Hughitt, K., Freij, N., Meszaros, T., Bennett, S.M., Malocha, M., Evans, J., Agrawal, A., Leonard, A.J., Robitaille, T.P., Mampaey, B., Campos-Rozo, J.I., Kirk, M.S.: 2015, SunPy – Python for solar physics. Comput. Sci. Discov. 8(1), 014009. DOI. ADS. CrossRefGoogle Scholar
  30. Nishizuka, N., Sugiura, K., Kubo, Y., Den, M., Watari, S., Ishii, M.: 2017, Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. Astrophys. J. 835, 156. DOI. ADS. ADSCrossRefGoogle Scholar
  31. Pesnell, W.D., Thompson, B.J., Chamberlin, P.C.: 2012, The Solar Dynamics Observatory (SDO). Solar Phys. 275, 3. DOI. ADS. ADSCrossRefGoogle Scholar
  32. Priest, E.R., Forbes, T.G.: 2002, The magnetic nature of solar flares. Astron. Astrophys. Rev. 10, 313. DOI. ADS. ADSCrossRefGoogle Scholar
  33. Rahimi, A., Recht, B.: 2008, Random features for large-scale kernel machines. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Adv. Neural Information Processing Systems, 20, 1177. Google Scholar
  34. Schou, J., Scherrer, P.H., Bush, R.I., Wachter, R., Couvidat, S., Rabello-Soares, M.C., Bogart, R.S., Hoeksema, J.T., Liu, Y., Duvall, T.L., Akin, D.J., Allard, B.A., Miles, J.W., Rairden, R., Shine, R.A., Tarbell, T.D., Title, A.M., Wolfson, C.J., Elmore, D.F., Norton, A.A., Tomczyk, S.: 2012, Design and ground calibration of the Helioseismic and Magnetic Imager (HMI) instrument on the Solar Dynamics Observatory (SDO). Solar Phys. 275, 229. DOI. ADS. ADSCrossRefGoogle Scholar
  35. Schrijver, C.J.: 2007, A characteristic magnetic field pattern associated with all major solar flares and its use in flare forecasting. Astrophys. J. Lett. 655, L117. DOI. ADS. ADSCrossRefGoogle Scholar
  36. Schwenn, R.: 2006, Space weather: the solar perspective. Living Rev. Solar Phys. 3, 2. DOI. ADS. ADSCrossRefGoogle Scholar
  37. Song, H., Tan, C., Jing, J., Wang, H., Yurchyshyn, V., Abramenko, V.: 2009, Statistical assessment of photospheric magnetic features in imminent solar flare predictions. Solar Phys. 254, 101. DOI. ADSCrossRefGoogle Scholar
  38. Su, Y., Golub, L., Van Ballegooijen, A.A.: 2007, A statistical study of shear motion of the footpoints in two-ribbon flares. Astrophys. J. 655, 606. DOI. ADS. ADSCrossRefGoogle Scholar
  39. Sudol, J.J., Harvey, J.W.: 2005, Longitudinal magnetic field changes accompanying solar flares. Astrophys. J. 635, 647. DOI. ADS. ADSCrossRefGoogle Scholar
  40. Sun, X.: 2013, On the coordinate system of Space-Weather HMI Active Region Patches (SHARPs): a technical note. arXiv. ADS.
  41. Turmon, M., Jones, H.P., Malanushenko, O.V., Pap, J.M.: 2010, Statistical feature recognition for multidimensional solar imagery. Solar Phys. 262, 277. DOI. ADS. ADSCrossRefGoogle Scholar
  42. Welsch, B.T., Li, Y., Schuck, P.W., Fisher, G.H.: 2009, What is the relationship between photospheric flow fields and solar flares? Astrophys. J. 705, 821. DOI. ADS. ADSCrossRefGoogle Scholar
  43. Wheatland, M.S.: 2004, A Bayesian approach to solar flare prediction. Astrophys. J. 609(2), 17. DOI. CrossRefGoogle Scholar
  44. Woodcock, F.: 1976, The evaluation of yes/no forecasts for scientific and administrative purposes. Mon. Weather Rev. 104, 1209. DOI. ADS. ADSCrossRefGoogle Scholar
  45. Yu, D., Huang, X., Wang, H., Cui, Y.: 2009, Short-term solar flare prediction using a sequential supervised learning method. Solar Phys. 255, 91. DOI. ADS. ADSCrossRefGoogle Scholar
  46. Yuan, Y., Shih, F.Y., Jing, J., Wang, H.-M.: 2010, Automated flare forecasting using a statistical learning technique. Res. Astron. Astrophys. 10, 785. DOI. ADS. ADSCrossRefGoogle Scholar
  47. Zirin, H., Wang, H.: 1993, Narrow lanes of transverse magnetic field in sunspots. Nature 363, 426. DOI. ADS. ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of CaliforniaBerkeleyUSA
  2. 2.W.W. Hansen Experimental Physics LaboratoryStanford UniversityStanfordUSA

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